The impact of e-marketing orientation, technological orientation and learning capacity on online SME performance by Ahmed Al Asheq, Khadiza Rahman Tanchi, Md. Kamruzzaman, Md. Mobarak Karim

“…Thus, the aim of the study is to examine the impact of e-marketing orientation (EMO), technology orientation (TO), and learning capability (LC) on online SME performance in the context of Bangladesh. …”
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Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance Using Reinforcement Learning by Muhammad Nawaz Khan, Sokjoon Lee, Mohsin Shah

“…Here in this article, we propose a reinforcement learning-based mechanism called “Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. …”
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Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China by Dewei Xie, Zhaopei Zheng, Xin Ding, Lihong Wei, Yu Lan

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Deep learning segmentation of soil constituents in 3D X-ray CT images by Maxime Phalempin, Lars Krämer, Maik Geers-Lucas, Fabian Isensee, Steffen Schlüter

“…In this study, we explore the potential of nnUNet, a deep learning-based semantic segmentation model, for applications in soil science. …”
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Client Selection in Federated Learning on Resource-Constrained Devices: A Game Theory Approach by Zohra Dakhia, Massimo Merenda

“…Federated Learning (FL), a key paradigm in privacy-preserving and distributed machine learning (ML), enables collaborative model training across decentralized data sources without requiring raw data exchange. …”
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Combining radiomics and deep learning to predict liver metastasis of gastric cancer on CT image by Yimin Guo, Yimin Guo, Haixiang Yin, Haixiang Yin, Hanyue Zhang, Hanyue Zhang, Pan Liang, Pan Liang, Jianbo Gao, Jianbo Gao, Ming Cheng, Ming Cheng

“…ObjectiveOur study aimed to explore the potential of deep learning (DL) radiomics features from CT images of primary gastric cancer (GC) in predicting gastric cancer liver metastasis (GCLM) by establishing and verifying a prediction model based on clinical factors, classical radiomics and DL features.MethodsWe retrospectively analyzed 1001 pathologically confirmed GC patients from June 2014 to May 2024, divided into non-LM (n=689) and LM groups (n=312). …”
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Decoupling Urban Street Attractiveness: An Ensemble Learning Analysis of Color and Visual Element Contributions by Tao Wu, Zeyin Chen, Siying Li, Peixue Xing, Ruhang Wei, Xi Meng, Jingkai Zhao, Zhiqiang Wu, Renlu Qiao

“…Our findings suggest that the vegetation ratio contributes the most to VAPS, but once greening surpasses a certain threshold, a “saturation effect” emerges and can no longer continuously enhance visual appeal. …”
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Deep learning–assisted diagnosis of acute mesenteric ischemia based on CT angiography images by Lei Song, Xuesong Zhang, Jian Zhang, Jie Wu, Jinkai Wang, Feng Wang

“…We aimed to develop a deep learning (DL) model based on CT angiography (CTA) imaging and clinical data to diagnose AMI.MethodsA retrospective study was conducted on 228 patients suspected of AMI, divided into training and test sets. …”
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An Ultrasound-based Machine Learning Model for Predicting Tumor-Infiltrating Lymphocytes in Breast Cancer by Boya Liu MM, Xiangrong Gu MM, Danling Xie MM, Bing Zhao BM, Dong Han MD, Yuli Zhang BM, Tao Li PhD, Jingqin Fang MD, PhD

“…This study aimed to develop and evaluate machine learning models using ultrasound-derived radiomics and clinical features to predict TIL levels in BC. …”
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Research on Plant RNA-Binding Protein Prediction Method Based on Improved Ensemble Learning by Hongwei Zhang, Yan Shi, Yapeng Wang, Xu Yang, Kefeng Li, Sio-Kei Im, Yu Han

“…Accurate prediction of plant-specific RBPs is vital for understanding gene regulation and enhancing genetic improvement. (2) Methods: We propose an ensemble learning method that integrates shallow and deep learning. …”
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A Deep Learning-Based Time-Frequency Scheme for Ship Detection Using HFSWR by Da Huang, Hao Zhou, Yingwei Tian, Zhiqing Yang, Weimin Huang

“…To address this challenge, a deep learning (DL)-based scheme tailored for identifying ship targets in the time-frequency (TF) domain is presented. …”
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Bringing Machine Learning Classifiers Into Critical Cyber-Physical Systems: A Matter of Design by Burcu Sayin, Tommaso Zoppi, Nicolo Marchini, Fahad Ahmed Khokhar, Andrea Passerini

“…Machine Learning (ML) models are increasingly used by domain experts to tackle classification tasks, aiming for high predictive accuracy. …”
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Detection and mapping of Antarctic lichen using drones, multispectral cameras, and supervised deep learning by Narmilan Amarasingam, Juan Sandino, Ashray Doshi, Diana King, Elka Blackman, Johan Barthelemy, Barbara Bollard, Sharon A. Robinson, Felipe Gonzalez

“…Few studies have explored the use of remote sensing and deep learning (DL) techniques for mapping and monitoring lichen density in Antarctic regions. …”
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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Feature selection was performed in two steps: first using univariate logistic regression, followed by multivariate logistic regression with a stringent significance threshold (p < 0.05). We utilized six machine learning algorithms—Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT)—to construct predictive models. …”
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External Validation of Persistent Severe Acute Kidney Injury Prediction With Machine Learning Model by Simone Zappalà, PhD, Francesca Alfieri, MS, Andrea Ancona, PhD, Antonio M. Dell’Anna, MD, Kianoush B. Kashani, MD, MS

“…Objective: To externally validate the persistent electronic alert (PersEA) machine learning model for predicting persistent severe acute kidney injury (psAKI), addressing the scarcity of validated prediction models. …”
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Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…Decision Curve Analysis (DCA) indicates that within the majority of threshold ranges, the MLP can achieve the highest net benefit. …”
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Effect of shear rate on early Shewanella oneidensis adhesion dynamics monitored by deep learning by Lucie Klopffer, Nicolas Louvet, Simon Becker, Jérémy Fix, Cédric Pradalier, Laurence Mathieu

“…Secondly, at the individual scale, by implementing an automated image processing method based on deep learning to track each individual pioneer bacterium on the wall. …”
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Deep learning for image-based detection of weeds from emergence to maturity in wheat fields by Mustafa Guzel, Bulent Turan, Izzet Kadioglu, Alper Basturk, Bahadir Sin, Amir Sadeghpour

“…The dataset was used with the YOLOv5 (You Only Look Once) deep learning architecture. The YOLOv5 was the newest YOLO model during this study carried out. …”
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MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children by Yasen Yimit, Yasen Yimit, Parhat Yasin, Yue Hao, Abudouresuli Tuersun, Abudouresuli Tuersun, Chencui Huang, Xiaoguang Zou, Xiaoguang Zou, Ya Qiu, Ya Qiu, Yunling Wang, Mayidili Nijiati, Mayidili Nijiati

“…For patient classification, we used two voting strategies: hard voting strategy in which the majority prediction was selected from individual image slices; soft voting strategy in which the prediction scores were averaged across slices with a threshold of 0.5. Additionally, a multimodality fusion model was constructed by integrating the DL classifier with clinical and imaging features. …”
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Research on Optimized Algorithm for Deep Learning Based Recognition of Sediment Particles in Turbulent Flow by WANG Hao, YANG Feiqi, ZHANG Lei, WU Wei, XIE Haonan, ZHAO Lin

“…This study integrates deep learning networks with existing image processing techniques to enable more precise and comprehensive identification of suspended sediment particles. …”
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